We are privileged to be working with companies spearheading their industries.
One such company is skyTran, a startup that is working on creating a private transportation system based on magnetic levitation (MagLev).
skyTran is a high-capacity, personal mass transit system that is taking aim at the world of gridlocked traffic.
Its on-demand, personal occupancy vehicles allow riders and cargo to travel above the road at freeway speeds and beyond.
Vehicles operate autonomously and use proprietary switching technology to safely navigate complex guideway networks direct-to-destination without the constraints of ground-based corridors.
Headquartered in Huntington Beach, California and founded in 2011, skyTran was awarded a grant by the Research and Innovation Technology Administration, which is part of the United States Department of Transportation.
Key results with Quanscient Allsolve:
Simulation time: Down from 3 weeks to 8 hours
Accuracy: Up from within 10% of experimental data to within 3%.
The work they do involves complex systems that require a thorough understanding and modeling of electromagnetic interactions.
Due to the large-scale nature of the simulations, the company was facing challenges finding adequate simulation software with full-scale device simulation capabilities.
"We needed a simulation platform to create a digital twin of all components of our system, especially our maglev technology," says Iana Volvach from skyTran.
In this blog article, we will take a closer look at skyTran’s use case and share the learnings they have made while using Quanscient Allsolve with their simulations.
The problem with existing solutions
skyTran operates in the field of magnetic levitation, popularly known as MagLev technology.
skyTran uses a modified and improved MagLev variant called electrodynamic suspension based on coupled levitation and propulsion systems.
Its propulsion comes from a motor with a magnetic rotor and aluminum stator. Levitation is achieved through electromagnets in the vehicle interacting with steel guideways.
Since their beginning, they have had to rely on simulations to assist with the research and development of the magnetic drive motor.
These simulations are inherently complex with a wide array of components and variables. They deal with an array of magnets, electromagnets, and their interactions with various steel and aluminum components within the system.
The scale of these simulations makes them incredibly resource-intensive and demand a level of computational power and speed that many popular software solutions simply cannot provide on in-house hardware setup.
The problem that skyTran faced was not just the complexity of the simulations. It was the need for software capable of handling the largeness of these simulations while remaining user-friendly.
Again, most solutions they tried were lacking in either one.
The ideal solution needed to allow for the simulation of their full-scale devices in a way that was efficient and easily accessible.
Onboarding and adoption of Quanscient Allsolve
Iana Volvach first came into contact with Quanscient’s CTO Dr. Alexandre Halbach about two years before Quanscient Allsolve was created.
"Alexandre, from Quanscient, told us that they were developing a solution that could tackle our problems. A year later, he reached out, and we decided to test Quanscient Allsolve's capabilities."
The testing phase for skyTran involved a hands-on trial where they used a real problem they were already solving to evaluate the software. They had a real device and experimental measurements to compare against Quanscient Allsolve's simulations.
The simulation model was represented with an array of 4 permanent elongated and radially oriented magnets located on the steel plate that was moving above the 2 aluminum plates at a distance of 5-20 mm.
During the motion (0-50 m/s) the steel plate with the magnets interacted with the static aluminum plates inducing the eddy currents in the last ones.
skyTran studied the magnetic forces and torques acting on the aluminum plates during the motion of the steel plate with the magnets.
At different speeds of the motion, they were able to calculate the magnetic forces and behavior of the system.
The team at skyTran also used Quanscient Allsolve to output and calculate the magnetic flux distribution in the ferromagnetic parts of the system and eddy currents on the conducting aluminum plates.
The system represented the simplified magnetic motor design intended to be used for the propulsion of skyTran's system.
Simulation model representation
The results were impressive, and within three months, the team had a full-scale model that matched their physical device's movements and outputs.
The ease of integrating Quanscient Allsolve into their workflow was another crucial factor in SkyTran's decision to adopt the software.
Unlike traditional software, Quanscient Allsolve doesn't require extensive installations or specific devices. It's accessible, user-friendly, and makes it easy for skyTran's engineers to start simulating almost immediately.
"In terms of transitioning, we started by using Quanscient Allsolve as an add-on to our existing software," Iana shares. "However, once we recognized the improvements in speed and convenience, we decided to move completely to Quanscient Allsolve."
When skyTran first started using Quanscient Allsolve, several significant improvements quickly became apparent.
First and foremost, the time reduction for their simulation processes was substantial. Compared to their previous software, the speed at which Quanscient Allsolve operated was substantially faster reducing the simulation time from 3 weeks to just 8 hours.
This time efficiency didn't just save them work-hours; it drastically increased the volume of work they could handle. They were able to run simulations simultaneously, making their overall work process far more effective.
This scale of performance enhancement transformed their work schedule from weeks to just a day or two for a similar workload.
Cost-wise, Quanscient Allsolve also proved to be a wise investment. The financial implications of the speed and efficiency Quanscient Allsolve provided were tremendous. They could pay for running up to 100 simulations without breaking the bank, something unimaginable with their previous setup.
The workflow was also noticeably smoother with Quanscient Allsolve. Its advanced post-processing capabilities gave skyTran the freedom to output data in a way that suited their needs best.
Unlike their previous software, where they had to output everything before processing, Quanscient Allsolve gave them the flexibility to output only the essential data, thereby saving them substantial storage space.
Still, perhaps the most important improvement with Quanscient Allsolve was its capability to simulate and visualize large-scale models with high accuracy.
With the ability to visualize models with 5 million nodes or elements, Quanscient Allsolve offered a degree of scalability that was unparalleled with increased accuracy from within 10% of experimental data to within 3%.
Last but not least, Quanscient Allsolve's cloud-based storage meant that skyTran's data was not only secure but also easily accessible from any location. This convenience factor was a significant improvement over their previous system.
These improvements proved to be major enhancements that made a difference in the day-to-day operations and the big-picture goals of skyTran. Their transition to Quanscient Allsolve was a strategic decision that resulted in improved efficiency, cost savings, and an overall better output.
Full support every step of the way
Throughout their adoption and usage of Quanscient Allsolve, skyTran felt well-supported by the Quanscient experts.
The company was provided with unlimited support during the initial stages, which enabled seamless collaboration and faster problem-solving. The responses from the Quanscient team were always prompt, even considering the time difference between the two companies.
skyTran appreciated the dedication and effort the Quanscient team put into understanding their unique challenges and helping solve them. Even when dealing with complex, high-level simulations, the Quanscient team invested their time and resources to deliver results quickly.
This level of support was a significant factor in building skyTran's confidence in Quanscient Allsolve, resulting in continued collaboration and growth.
Quanscient Allsolve in large-scale multiphysics simulations
Iana would recommend Quanscient Allsolve to manufacturing companies and others who need to simulate large-scale, multiphysics problems.
Especially for those who deal with simulations that would traditionally take a week or longer, the speed and efficiency offered by Quanscient Allsolve can be a game-changer.
Iana believes that simulation is an invaluable tool in modern engineering, as it can help companies save time, resources, and potentially a significant amount of money. Building hardware without simulations can lead to costly errors and setbacks.
Quanscient Allsolve brings confidence to the process, assuring that you know what will work before you build it.
For companies like skyTran, who are developing new technologies and need to simulate large-scale problems, Quanscient Allsolve is indeed an excellent choice.
The software is particularly well-suited for large-scale problems and statistical analysis because it's cloud-based and can run multiple simulations simultaneously.
skyTran's experience with Quanscient Allsolve demonstrates the impact of the right simulation software on complex engineering projects
Their significant reduction in time and cost, increased ease of use, and the ability to manage large-scale simulations have had a huge impact on their workflow.
The responsiveness and the high level of support offered by the Quanscient team have been pivotal in their successful adoption and utilization of the software.
skyTran recommends Quanscient Allsolve to companies working with large-scale, multiphysics simulations.
To find out if Quanscient Allsolve could be a match with your use case, book a call!